The present network for extracting biological echoes from weather radar includes limitations such as a large number of parameters, low efficiency, and the requirement for a large number of samples. This paper offers an improved biological echo extraction network called MFF-PSPnet (Multi-Feature Fusion Pyramid Scene Parsing Network). This network introduces the attention mechanism module and edge extraction module based on the MobilenetV2 backbone, with all features fused for recognition. This modification increases the network's segmentation capabilities in two ways: biological echo main body extraction and biological echo edge detail characterisation. MFF-PSPnet is a lightweight network that uses 10.2% fewer parameters than the conventional biological echo extraction network. Compared to previous models, this network requires less sample capacity and is better suited to small sample situations. On the historical data set, MFF-PSPnet extracted biological echoes with an accuracy of 98.1% and an IoU of 94.8%. The experimental results show that in the historical data of the new generation of Chinese weather radar, the improved model in this work can efficiently extract biological echoes, adapt better to the limited sample environment, and be used to mobile devices. This finding gives useful data and technological support for the future application of weather radar for biological behavior study and protection.